In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal reactive power problem. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents’ numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.
In this paper Enhanced whale Optimization Algorithm (EWO) proposed to solve the optimal reactive power problem. Whale optimization algorithm is modeled by Bubble-net hunting tactic. In the projected optimization algorithm an inertia weight ω ∈ [1, 0] has been introduced to perk up the search ability. Whales are commonly moving 10-16 meters down then through the bubbles which are created artificially then they encircle the prey and move upward towards the surface of sea. Proposed Enhanced whale optimization algorithm (EWO) is tested in standard IEEE 57 bus systems and power loss reduced considerably.
This paper proposes Arctic Wolf optimization (AWO) algorithm to solve the optimal reactive power problem. Deeds of the Arctic wolf have been imitated to formulate the proposed algorithm. Arctic wolf also identified as the white wolf or polar wolf is a breed of gray wolf inhabitant from Melville Island to Ellesmere Island. It is average size, very smaller when compared to north western wolf, it possess whiter coloration, narrower braincase and big carnassials. Particle swarm optimization, Genetic algorithm has been used to improve the Exploration & Exploitation ability of the algorithm by utilizes flag vector & position, velocity update properties. Proposed Arctic Wolf optimization (AWO) algorithm has been tested in standard IEEE 30 bus test system and simulation results show the projected algorithms reduced the real power loss considerably.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Optimal Load Dispatch Using Ant Lion OptimizationIJERA Editor
This paper presents Ant lion optimization (ALO) technique to solve optimal load dispatch problem. Ant lion
optimization (ALO) is a novel nature inspired algorithm. The ALO algorithm mimics the hunting mechanism of
ant lions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of
ants in traps, catching preys, and re-building traps are implemented. Optimal load dispatch (OLD) is a method of
determining the most efficient, low-cost and reliable operation of a power system by dispatching available
electricity generation resources to supply load on the system. The primary objective of OLD is to minimize total
cost of generation while honoring operational constraints of available generation resources. The proposed
technique is implemented on 3, 6 & 20 unit test system for solving the OLD. Numerical results shows that the
proposed method has good convergence property and better in quality of solution than other algorithms reported
in recent literature.
In this paper Enhanced Wormhole Optimizer (EWO) algorithm is used to solve optimal reactive power problem. Proposed algorithm based on the Wormholes which exploits the exploration space. Between different universes objects are exchanged through white or black hole tunnels. Regardless of the inflation rate, through wormholes objects in all universes which possess high probability will shift to the most excellent universe. In the projected Enhanced Wormhole Optimizer (EWO) algorithm in order to avoid the solution to be get trapped into the local optimal solution Levy flight has been applied. Projected Enhanced Wormhole Optimizer (EWO) algorithm has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show that the EWO algorithm reduced the real power loss efficiently.
In this paper Enhanced whale Optimization Algorithm (EWO) proposed to solve the optimal reactive power problem. Whale optimization algorithm is modeled by Bubble-net hunting tactic. In the projected optimization algorithm an inertia weight ω ∈ [1, 0] has been introduced to perk up the search ability. Whales are commonly moving 10-16 meters down then through the bubbles which are created artificially then they encircle the prey and move upward towards the surface of sea. Proposed Enhanced whale optimization algorithm (EWO) is tested in standard IEEE 57 bus systems and power loss reduced considerably.
This paper proposes Arctic Wolf optimization (AWO) algorithm to solve the optimal reactive power problem. Deeds of the Arctic wolf have been imitated to formulate the proposed algorithm. Arctic wolf also identified as the white wolf or polar wolf is a breed of gray wolf inhabitant from Melville Island to Ellesmere Island. It is average size, very smaller when compared to north western wolf, it possess whiter coloration, narrower braincase and big carnassials. Particle swarm optimization, Genetic algorithm has been used to improve the Exploration & Exploitation ability of the algorithm by utilizes flag vector & position, velocity update properties. Proposed Arctic Wolf optimization (AWO) algorithm has been tested in standard IEEE 30 bus test system and simulation results show the projected algorithms reduced the real power loss considerably.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Optimal Load Dispatch Using Ant Lion OptimizationIJERA Editor
This paper presents Ant lion optimization (ALO) technique to solve optimal load dispatch problem. Ant lion
optimization (ALO) is a novel nature inspired algorithm. The ALO algorithm mimics the hunting mechanism of
ant lions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of
ants in traps, catching preys, and re-building traps are implemented. Optimal load dispatch (OLD) is a method of
determining the most efficient, low-cost and reliable operation of a power system by dispatching available
electricity generation resources to supply load on the system. The primary objective of OLD is to minimize total
cost of generation while honoring operational constraints of available generation resources. The proposed
technique is implemented on 3, 6 & 20 unit test system for solving the OLD. Numerical results shows that the
proposed method has good convergence property and better in quality of solution than other algorithms reported
in recent literature.
In this paper Enhanced Wormhole Optimizer (EWO) algorithm is used to solve optimal reactive power problem. Proposed algorithm based on the Wormholes which exploits the exploration space. Between different universes objects are exchanged through white or black hole tunnels. Regardless of the inflation rate, through wormholes objects in all universes which possess high probability will shift to the most excellent universe. In the projected Enhanced Wormhole Optimizer (EWO) algorithm in order to avoid the solution to be get trapped into the local optimal solution Levy flight has been applied. Projected Enhanced Wormhole Optimizer (EWO) algorithm has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show that the EWO algorithm reduced the real power loss efficiently.
An optimisation-based energy disaggregation algorithm for low frequency smart...encompassH2020
An optimisation-based energy disaggregation algorithm for low frequency smart meter data. Presented at the 8th DACH+ Conference on Energy Informatics, Salzburg, Austria, September 2019
PSOk-NN: A Particle Swarm Optimization Approach to Optimize k-Nearest Neighbo...Aboul Ella Hassanien
This talk presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.
Long-term outdoor localisation with battery-powered devices remains an unsolved challenge,mainly due to the high energy consumption of GPS modules. The use of inertial sensors and short-range radio can reduce reliance on GPS to prolong the operational lifetime of tracking devices, butthey only provide coarse-grained control over GPS activity. An alternative yet promising approach is touse context-sensitive mobility models to guide scheduling and sampling decisions in localisationalgorithms. In this talk, I will present our work towards continental-scale long-term tracking of flyingfoxes, as part of the National Flying Fox Monitoring Program in Australia, using a model-drivenapproach. At the core of our approach is the multimodal GPS-enabled Camazotz sensor node platformthat has been designed at CSIRO for flying fox collars, with a cumulative weight just under 30g. The project has already deployed tens of devices on live flying foxes, which have been operating in thefield for several months. We are using the data from these devices to build mobility models andalgorithms for designing the next generation of software, as we will progressively deploy more than1000 nodes within the coming months. The progressive deployment of nodes coupled with delaytolerance, constrained resources, and incremental feature development raises interesting systemschallenges and opportunities, which I will highlight. The talk will also provide a snapshot of thecurrent data collection effort, and draw lessons from our activities in this area over the past 18 months
This report exposed the computational process of ltering a noisy three-dimensional ultra-sound using Fourier Transform to locate an object. The resolution of this localisation problemwas done in three steps. The rst one was nding the object position in the frequency do-main.The second step was to lter the data or precisely centered the data frequencies aroundthe object frequency. Finally, the last step was to translate the ltered frequencies into thespatial domain and locate the object.
Analysis of large scale spiking networks dynamics with spatio-temporal constr...Hassan Nasser
Recent experimental advances have made it possible to record up to several hundreds of neurons simultaneously in the cortex or in the retina. Analysing such data requires mathematical and numerical methods to describe the spatio-temporal correlations in population activity. This can be done thanks to Maximum Entropy method. Here, a crucial parameter is the product NxR where N is the number of neurons and R the memory depth of correlations (how far in the past does the spike activity affects the current state). Standard statistical mechanics methods are limited to spatial correlation structure with
R = 1 (e.g. Ising model) whereas methods based on transfer matrices, allowing the analysis of spatio-temporal correlations, are limited to NR = 20.
In the first part of the thesis we propose a modified version of the transfer matrix method, based on the parallel version of the Montecarlo algorithm, allowing us to go to NR = 100.
In the second part we present EnaS, a C++ library with a Graphical User Interface developed for neuroscientists. EnaS offers highly interactive tools that allow users to manage data, perform empirical statistics, modeling and visualizing results.
Finally, in a third part, we test our method on synthetic and real data sets. Real data set correspond to retina data provided by neuroscientists partners. Our non extensive analysis shows the advantages of considering spatio-temporal correlations for the analysis of retina spike trains, but it also outlines the limits of Maximum Entropy methods.
For more information about the software that I co-developed with my colleagues, please visit this page:
https://enas.inria.fr/
For more information about the publications, please visit this page:
https://scholar.google.fr/citations?user=L97ZODwAAAAJ
For the thesis, please visit this link:
https://www.theses.fr/178166669
Model Predictive Control based on Reduced-Order ModelsPantelis Sopasakis
The need for reduced-order approximations of dynamical systems emerges naturally in model-based control of very large-scale systems, such as those arising from the discretisation of partial differential equation models. The controller based on the reduced-order model, when in closed-loop with the large-scale system, ought to endow certain properties, in primis stability, but also satisfaction of state constraints and recursive computability of the control law in the case of constrained control.
In this paper we introduce a new approach to the design of model predictive controllers to meet the aforementioned requirements while the on-line complexity is essentially tantamount to the one that corresponds to the low-dimensional approximate model.
This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.
Chaotic based Pteropus algorithm for solving optimal reactive power problemIJAAS Team
In this work, a Chaotic based Pteropus algorithm (CPA) has been proposed for solving optimal reactive power problem. Pteropus algorithm imitates deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles by using sonar echoes, particularly utilize time delay. To the original Pteropus algorithm chaotic disturbance has been applied and the optimal capability of the algorithm has been improved in search of global solution. In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance is added at the time of stagnation. Furthermore, exploration and exploitation capability of the proposed algorithm has been improved. Proposed CPA technique has been tested in standard IEEE 14,300 bus systems & real power loss has been considerably reduced.
Economic Load Dispatch Using Grey Wolf OptimizationIJERA Editor
This paper presents grey wolf optimization (GWO) to solve convex economic load dispatch (ELD) problem. Grey
Wolf Optimization (GWO) is a new meta-heuristic inspired by grey wolves. The leadership hierarchy and hunting
mechanism of the grey wolves is mimicked in GWO. The objective of ELD problem is to minimize the total
generation cost while fulfilling the different constraints, when the required load of power system is being
supplied. The proposed technique is implemented on two different test systems for solving the ELD with various
load demands. To show the effectiveness of GWO to solve ELD problem results were compared with other
existing techniques.
ABSTRACT: In this paper, we proposed a new identification algorithm based on Kolmogorov–Zurbenko Periodogram (KZP) to separate motions in spatial motion image data. The concept of directional periodogram is utilized to sample the wave field and collect information of motion scales and directions. KZ Periodogram enables us detecting precise dominate frequency information of spatial waves covered by highly background noises. The computation of directional periodogram filters out most of the noise effects, and the procedure is robust for missing and fraud spikes caused by noise and measurement errors. This design is critical for the closure-based clustering method to find cluster structures of potential parameter solutions in the parameter space. An example based on simulation data is given to demonstrate the four steps in the procedure of this method. Related functions are implemented in our recent published R package {kzfs}.
An improved ant colony algorithm based onIJCI JOURNAL
This paper presents an improved chaotic ant colony system algorithm (ICACS) for solving combinatorial
optimization problems. The existing algorithms still have some imperfections, we use a combination of two
different operators to improve the performance of algorithm in this work. First, 3-opt local search is used
as a framework for the implementation of the ACS to improve the solution quality; Furthermore, chaos is
proposed in the work to modify the method of pheromone update to avoid the algorithm from dropping into
local optimum, thereby finding the favorable solutions. From the experimental results, we can conclude
that ICACS has much higher quality solutions than the original ACS, and can jump over the region of the
local optimum, and escape from the trap of a local optimum successfully and achieve the best solutions.
Therefore, it’s better and more effective algorithm for TSP.
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIREditor IJMTER
Suppose a composite system consisting of two subsystems designated as ‘P’ and
‘Q’ connected in series. Subsystem ‘P’ consists of N non-identical units in series, while the
subsystem ‘Q’ consists of three identical components in parallel redundancy.
Passerine swarm optimization algorithm for solving optimal reactive power dis...IJAAS Team
This paper presents Passerine Swarm Optimization Algorithm (PSOA) for solving optimal reactive power dispatch problem. This algorithm is based on behaviour of social communications of Passerine bird. Basically, Passerine bird has three common behaviours: search behaviour, adherence behaviour and expedition behaviour. Through the shared communications Passerine bird will search for the food and also run away from hunters. By using the Passerine bird communications and behaviour, five basic rules have been created in the PSOA approach to solve the optimal reactive power dispatch problem. Key aspect is to reduce the real power loss and also to keep the variables within the limits. Proposed Passerine Swarm Optimization Algorithm (PSOA) has been tested in standard IEEE 30 bus test system and simulations results reveal about the better performance of the proposed algorithm in reducing the real power loss and enhancing the static voltage stability margin.
Platoon Control of Nonholonomic Robots using Quintic Bezier SplinesKaustav Mondal
In this project, quintic polynomials were used to perform platooning in nonholonomic robots. Both hardware and simulations results have been presented.
An optimisation-based energy disaggregation algorithm for low frequency smart...encompassH2020
An optimisation-based energy disaggregation algorithm for low frequency smart meter data. Presented at the 8th DACH+ Conference on Energy Informatics, Salzburg, Austria, September 2019
PSOk-NN: A Particle Swarm Optimization Approach to Optimize k-Nearest Neighbo...Aboul Ella Hassanien
This talk presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.
Long-term outdoor localisation with battery-powered devices remains an unsolved challenge,mainly due to the high energy consumption of GPS modules. The use of inertial sensors and short-range radio can reduce reliance on GPS to prolong the operational lifetime of tracking devices, butthey only provide coarse-grained control over GPS activity. An alternative yet promising approach is touse context-sensitive mobility models to guide scheduling and sampling decisions in localisationalgorithms. In this talk, I will present our work towards continental-scale long-term tracking of flyingfoxes, as part of the National Flying Fox Monitoring Program in Australia, using a model-drivenapproach. At the core of our approach is the multimodal GPS-enabled Camazotz sensor node platformthat has been designed at CSIRO for flying fox collars, with a cumulative weight just under 30g. The project has already deployed tens of devices on live flying foxes, which have been operating in thefield for several months. We are using the data from these devices to build mobility models andalgorithms for designing the next generation of software, as we will progressively deploy more than1000 nodes within the coming months. The progressive deployment of nodes coupled with delaytolerance, constrained resources, and incremental feature development raises interesting systemschallenges and opportunities, which I will highlight. The talk will also provide a snapshot of thecurrent data collection effort, and draw lessons from our activities in this area over the past 18 months
This report exposed the computational process of ltering a noisy three-dimensional ultra-sound using Fourier Transform to locate an object. The resolution of this localisation problemwas done in three steps. The rst one was nding the object position in the frequency do-main.The second step was to lter the data or precisely centered the data frequencies aroundthe object frequency. Finally, the last step was to translate the ltered frequencies into thespatial domain and locate the object.
Analysis of large scale spiking networks dynamics with spatio-temporal constr...Hassan Nasser
Recent experimental advances have made it possible to record up to several hundreds of neurons simultaneously in the cortex or in the retina. Analysing such data requires mathematical and numerical methods to describe the spatio-temporal correlations in population activity. This can be done thanks to Maximum Entropy method. Here, a crucial parameter is the product NxR where N is the number of neurons and R the memory depth of correlations (how far in the past does the spike activity affects the current state). Standard statistical mechanics methods are limited to spatial correlation structure with
R = 1 (e.g. Ising model) whereas methods based on transfer matrices, allowing the analysis of spatio-temporal correlations, are limited to NR = 20.
In the first part of the thesis we propose a modified version of the transfer matrix method, based on the parallel version of the Montecarlo algorithm, allowing us to go to NR = 100.
In the second part we present EnaS, a C++ library with a Graphical User Interface developed for neuroscientists. EnaS offers highly interactive tools that allow users to manage data, perform empirical statistics, modeling and visualizing results.
Finally, in a third part, we test our method on synthetic and real data sets. Real data set correspond to retina data provided by neuroscientists partners. Our non extensive analysis shows the advantages of considering spatio-temporal correlations for the analysis of retina spike trains, but it also outlines the limits of Maximum Entropy methods.
For more information about the software that I co-developed with my colleagues, please visit this page:
https://enas.inria.fr/
For more information about the publications, please visit this page:
https://scholar.google.fr/citations?user=L97ZODwAAAAJ
For the thesis, please visit this link:
https://www.theses.fr/178166669
Model Predictive Control based on Reduced-Order ModelsPantelis Sopasakis
The need for reduced-order approximations of dynamical systems emerges naturally in model-based control of very large-scale systems, such as those arising from the discretisation of partial differential equation models. The controller based on the reduced-order model, when in closed-loop with the large-scale system, ought to endow certain properties, in primis stability, but also satisfaction of state constraints and recursive computability of the control law in the case of constrained control.
In this paper we introduce a new approach to the design of model predictive controllers to meet the aforementioned requirements while the on-line complexity is essentially tantamount to the one that corresponds to the low-dimensional approximate model.
This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.
Chaotic based Pteropus algorithm for solving optimal reactive power problemIJAAS Team
In this work, a Chaotic based Pteropus algorithm (CPA) has been proposed for solving optimal reactive power problem. Pteropus algorithm imitates deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles by using sonar echoes, particularly utilize time delay. To the original Pteropus algorithm chaotic disturbance has been applied and the optimal capability of the algorithm has been improved in search of global solution. In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance is added at the time of stagnation. Furthermore, exploration and exploitation capability of the proposed algorithm has been improved. Proposed CPA technique has been tested in standard IEEE 14,300 bus systems & real power loss has been considerably reduced.
Economic Load Dispatch Using Grey Wolf OptimizationIJERA Editor
This paper presents grey wolf optimization (GWO) to solve convex economic load dispatch (ELD) problem. Grey
Wolf Optimization (GWO) is a new meta-heuristic inspired by grey wolves. The leadership hierarchy and hunting
mechanism of the grey wolves is mimicked in GWO. The objective of ELD problem is to minimize the total
generation cost while fulfilling the different constraints, when the required load of power system is being
supplied. The proposed technique is implemented on two different test systems for solving the ELD with various
load demands. To show the effectiveness of GWO to solve ELD problem results were compared with other
existing techniques.
ABSTRACT: In this paper, we proposed a new identification algorithm based on Kolmogorov–Zurbenko Periodogram (KZP) to separate motions in spatial motion image data. The concept of directional periodogram is utilized to sample the wave field and collect information of motion scales and directions. KZ Periodogram enables us detecting precise dominate frequency information of spatial waves covered by highly background noises. The computation of directional periodogram filters out most of the noise effects, and the procedure is robust for missing and fraud spikes caused by noise and measurement errors. This design is critical for the closure-based clustering method to find cluster structures of potential parameter solutions in the parameter space. An example based on simulation data is given to demonstrate the four steps in the procedure of this method. Related functions are implemented in our recent published R package {kzfs}.
An improved ant colony algorithm based onIJCI JOURNAL
This paper presents an improved chaotic ant colony system algorithm (ICACS) for solving combinatorial
optimization problems. The existing algorithms still have some imperfections, we use a combination of two
different operators to improve the performance of algorithm in this work. First, 3-opt local search is used
as a framework for the implementation of the ACS to improve the solution quality; Furthermore, chaos is
proposed in the work to modify the method of pheromone update to avoid the algorithm from dropping into
local optimum, thereby finding the favorable solutions. From the experimental results, we can conclude
that ICACS has much higher quality solutions than the original ACS, and can jump over the region of the
local optimum, and escape from the trap of a local optimum successfully and achieve the best solutions.
Therefore, it’s better and more effective algorithm for TSP.
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIREditor IJMTER
Suppose a composite system consisting of two subsystems designated as ‘P’ and
‘Q’ connected in series. Subsystem ‘P’ consists of N non-identical units in series, while the
subsystem ‘Q’ consists of three identical components in parallel redundancy.
Passerine swarm optimization algorithm for solving optimal reactive power dis...IJAAS Team
This paper presents Passerine Swarm Optimization Algorithm (PSOA) for solving optimal reactive power dispatch problem. This algorithm is based on behaviour of social communications of Passerine bird. Basically, Passerine bird has three common behaviours: search behaviour, adherence behaviour and expedition behaviour. Through the shared communications Passerine bird will search for the food and also run away from hunters. By using the Passerine bird communications and behaviour, five basic rules have been created in the PSOA approach to solve the optimal reactive power dispatch problem. Key aspect is to reduce the real power loss and also to keep the variables within the limits. Proposed Passerine Swarm Optimization Algorithm (PSOA) has been tested in standard IEEE 30 bus test system and simulations results reveal about the better performance of the proposed algorithm in reducing the real power loss and enhancing the static voltage stability margin.
Platoon Control of Nonholonomic Robots using Quintic Bezier SplinesKaustav Mondal
In this project, quintic polynomials were used to perform platooning in nonholonomic robots. Both hardware and simulations results have been presented.
Real power loss reduction by arctic char algorithmIJAAS Team
This work presents Arctic Char Algorithm (ACA) for solving optimal reactive power problem. In North America movement of Arctic char phenomenon is one among the twelve-monthly innate actions. Deeds of Arctic char have been imitated to design the algorithm. In stochastic mode solutions are initialized with one segment on every side of to the route ascendancy; particularly in between lower bound and upper bounds. Previous to the movement, Arctic char come to a decision about the passageway based on their perception. This implies stochastic mix up of control parameters to push the Arctic char groups (preliminary solution) in mutual pathway (evolutionary operators). Projected Arctic Char Algorithm (ACA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.
First results from the full-scale prototype for the Fluorescence detector Arr...Toshihiro FUJII
The Fluorescence detector Array of Single-pixel Telescopes (FAST) is a design concept for the next generation of ultrahigh-energy cosmic ray (UHECR) observatories, addressing the requirements for a large-area, low-cost detector suitable for measuring the properties of the highest energy cosmic rays. In the FAST design, a large field of view is covered by a few pixels at the focal plane of a mirror or Fresnel lens. Motivated by the successful detection of UHECRs using a prototype comprised of a single 200 mm photomultiplier-tube and a 1 m2 Fresnel lens system [Astropart.Phys. 74 (2016) 64-72], we have developed a new full-scale prototype consisting of four 200 mm photomultiplier-tubes at the focus of a segmented mirror of 1.6 m in diameter. In October 2016 we installed the full-scale prototype at the Telescope Array site in central Utah, USA, and began steady data taking. We report on first results of the full-scale FAST prototype, including measurements of artificial light sources, distant ultraviolet lasers, and UHECRs.
35th International Cosmic Ray Conference — ICRC2017 18th July, 2017
Bexco, Busan, Korea
Computing the masses of hyperons and charmed baryons from Lattice QCDChristos Kallidonis
Poster presented at the Computational Sciences 2013 Conference (Winner of poster competition). We present results on the masses of all forty light, strange and charm baryons from Lattice QCD simulations, focusing particularly on the computational aspects and requirements of such calculations.
This paper studies an approximate dynamic programming (ADP) strategy of a group of nonlinear switched systems, where the external disturbances are considered. The neural network (NN) technique is regarded to estimate the unknown part of actor as well as critic to deal with the corresponding nominal system. The training technique is simul-taneously carried out based on the solution of minimizing the square error Hamilton function. The closed system’s tracking error is analyzed to converge to an attraction region of origin point with the uniformly ultimately bounded (UUB) description. The simulation results are implemented to determine the effectiveness of the ADP based controller.
α Nearness ant colony system with adaptive strategies for the traveling sales...ijfcstjournal
On account of ant colony algorithm easy to fall into local optimum, this paper presents an improved ant
colony optimization called α-AACS and reports its performance. At first, we provide an concise description
of the original ant colony system(ACS) and introduce α-nearness based on the minimum 1-tree for ACS’s
disadvantage, which better reflects the chances of a given link being a member of an optimal tour. Then, we
improve α-nearness by computing a lower bound and propose other adaptations for ACS. Finally, we
conduct a fair competition between our algorithm and others. The results clearly show that α-AACS has a
better global searching ability in finding the best solutions, which indicates that α-AACS is an effective
approach for solving the traveling salesman problem.
Analysis of multipath channel delay estimation using subspace fittingTarik Kazaz
The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.
Similar to 05 20261 real power loss reduction (20)
In our homes or offices, security has been a vital issue. Control of home security system remotely always offers huge advantages like the arming or disarming of the alarms, video monitoring, and energy management control apart from safeguarding the home free up intruders. Considering the oldest simple methods of security that is the mechanical lock system that has a key as the authentication element, then an upgrade to a universal type, and now unique codes for the lock. The recent advancement in the communication system has brought the tremendous application of communication gadgets into our various areas of life. This work is a real-time smart doorbell notification system for home Security as opposes of the traditional security methods, it is composed of the doorbell interfaced with GSM Module, a GSM module would be triggered to send an SMS to the house owner by pressing the doorbell, the owner will respond to the guest by pressing a button to open the door, otherwise, a message would be displayed to the guest for appropriate action. Then, the keypad is provided for an authorized person for the provision of password for door unlocking, if multiple wrong password attempts were made to unlock, a message of burglary attempt would be sent to the house owner for prompt action. The main benefit of this system is the uniqueness of the incorporation of the password and messaging systems which denies access to any unauthorized personality and owner's awareness method.
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05 20261 real power loss reduction
1. International Journal of Informatics and Communication Technology (IJ-ICT)
Vol.9, No.2, August 2020, pp. 100~104
ISSN: 2252-8776, DOI: 10.11591/ijict.v9i2.pp100-104 100
Journal homepage: http://ijict.iaescore.com
Real power loss reduction by tundra wolf algorithm
Kanagasabai Lenin
Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India
Article Info ABSTRACT
Article history:
Received Nov 16, 2019
Revised Jan 17, 2020
Accepted Feb 11, 2020
In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal
reactive power problem. In the projected Tundra wolf algorithm (TWA) in
order to avoid the searching agents from trapping into the local optimal the
converging towards global optimal is divided based on two different
conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra
wolf has been taken as searching agent as an alternative of indebted to pursue
the first three most excellent candidates. Escalating the searching agents’
numbers will perk up the exploration capability of the Tundra wolf wolves in
an extensive range. Proposed Tundra wolf algorithm (TWA) has been tested
in standard IEEE 14, 30 bus test systems and simulation results show the
proposed algorithm reduced the real power loss effectively.
Keywords:
Optimal reactive power
Transmission loss
Tundra wolf algorithm
This is an open access article under the CC BY-SA license.
Corresponding Author:
Kanagasabai Lenin,
Department of EEE,
Prasad V. Potluri Siddhartha Institute of Technology,
Kanuru, Vijayawada, Andhra Pradesh, 520007, India.
Email: gklenin@gmail.com
1. INTRODUCTION
Reactive power problem plays an important role in secure and economic operations of power
system. Numerous types of methods [1-6] have been utilized to solve the optimal reactive power problem.
However many scientific difficulties are found while solving problem due to an assortment of constraints.
Evolutionary techniques [7-17] are applied to solve the reactive power problem. This paper proposes Tundra
wolf algorithm (TWA) to solve optimal reactive power problem. At first, searching agents has been
aggravated to scatter all over the extensive range of probing space to discover the probable prey as an
alternative of crowding in the region of the regular local optimal. This phase is also termed as exploration
period. In the subsequent exploitation phase, searching agents should have the ability to influence the
information of the probable prey to converge in the direction of the global optimal value. In general tracking
or hunting action is solitary possessed alpha, beta and delta Tundra wolf while the remaining Tundra wolves
are indebted to go behind them that include omega Tundra wolf. In sequence to augment the exploration
capability of the search agents, several alterations have been suggested. In the proposed Tundra wolf
algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to
pursue the first three most excellent candidates. Proposed Tundra wolf algorithm (TWA) has been tested in
standard IEEE 14, 30, bus test systems and simulation results show the projected algorithm reduced the real
power loss effectively.
2. Int J Inf & Commun Technol ISSN: 2252-8776
Real power loss reduction by tundra wolf algorithm (Kanagasabai Lenin)
101
2. PROBLEM FORMULATION
Objective of the problem is to reduce the true power loss:
F = PL = ∑ gkk∈Nbr (Vi
2
+ Vj
2
− 2ViVjcosθij) (1)
Voltage deviation given as follows:
F = PL + ωv × Voltage Deviation (2)
Voltage deviation given by:
Voltage Deviation = ∑ |Vi − 1|
Npq
i=1 (3)
Constraint (Equality),
PG = PD + PL (4)
Constraints (Inequality),
Pgslack
min
≤ Pgslack ≤ Pgslack
max
(5)
Qgi
min
≤ Qgi ≤ Qgi
max
, i ∈ Ng (6)
Vi
min
≤ Vi ≤ Vi
max
, i ∈ N (7)
Ti
min
≤ Ti ≤ Ti
max
, i ∈ NT (8)
Qc
min
≤ Qc ≤ QC
max
, i ∈ NC (9)
3. TUNDRA WOLF ALGORITHM
In the proposed Tundra wolf algorithm (TWA) hunting behavior of the Tundra wolf has been
imitated to design the algorithm for solving the optimal reactive power problem. In Tundra wolf algorithm,
the movement of wolf is described by,
𝐷̅ = |𝐶̅ 𝑋̅ 𝑝(𝑡) − 𝑋̅(𝑡)| (10)
𝑋̅(𝑡 + 1) = 𝑋̅ 𝑝(𝑡) − 𝐴⃗ ∙ 𝐷⃗⃗⃗ (11)
𝐴⃗ = 2𝑎. 𝑟1 − 𝑎 (12)
𝐶⃗ = 2. 𝑟2 (13)
𝑎 = 2 − 2𝑡 𝑡 𝑚𝑎𝑥⁄ (14)
The state of wolves are adjusted by,
𝐷 𝛼
⃗⃗⃗⃗⃗⃗ = |𝐶1
⃗⃗⃗⃗⃗, 𝑋 𝛼
⃗⃗⃗⃗⃗ − 𝑋⃗| (15)
𝐷𝛽
⃗⃗⃗⃗⃗ = |𝐶2
⃗⃗⃗⃗⃗, 𝑋𝛽
⃗⃗⃗⃗⃗ − 𝑋⃗| (16)
𝐷𝛾
⃗⃗⃗⃗⃗ = |𝐶3
⃗⃗⃗⃗⃗, 𝑋 𝛿
⃗⃗⃗⃗⃗ − 𝑋⃗| (17)
When the value of “A” are located in [-1, 1] capriciously, which indicate the procedure of local
search perceptibly in this phase the Tundra wolves attack towards the prey. Tundra wolves are forced to
make a global search When | A | > 1. Through the parameter “a” fluctuation range of “A” can be decreased.
3. ISSN: 2252-8776
Int J Inf & Commun Technol, Vol. 9, No. 2, August 2020: 100 – 104
102
In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping
into the local optimal the converging towards global optimal is divided based on two different conditions. At
first, searching agents has been aggravated to scatter all over the extensive range of probing space to discover
the probable prey as an alternative of crowding in the region of the regular local optimal. This phase is also
termed as exploration period. In the subsequent exploitation phase, searching agents should have the ability
to influence the information of the probable prey to converge in the direction of the global optimal value.
𝑎⃗ = 2 − 1 ∗ (
2
𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑖𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛
) (18)
In general tracking or hunting action is solitary possessed alpha, beta and delta Tundra wolf while
the remaining Tundra wolves are indebted to go behind them that include omega Tundra wolf. In sequence to
augment the exploration capability of the search agents, several alterations have been suggested. In the
proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an
alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents
numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Also it
makes the search agents to spread widely during exploration phase. The mode of hunting action done by
Tundra wolf will increase the efficiency and time will be saved.
𝐷 𝛼
⃗⃗⃗⃗⃗⃗ = |𝐶1
⃗⃗⃗⃗⃗, 𝑋 𝛼
⃗⃗⃗⃗⃗ − 𝑋⃗| (19)
𝐷𝛽
⃗⃗⃗⃗⃗ = |𝐶2
⃗⃗⃗⃗⃗, 𝑋𝛽
⃗⃗⃗⃗⃗ − 𝑋⃗| (20)
𝐷𝛾
⃗⃗⃗⃗⃗ = |𝐶3
⃗⃗⃗⃗⃗, 𝑋 𝛿
⃗⃗⃗⃗⃗ − 𝑋⃗| (21)
𝐷 𝜔
⃗⃗⃗⃗⃗⃗ = |𝐶4
⃗⃗⃗⃗⃗, 𝑋 𝜔
⃗⃗⃗⃗⃗⃗ − 𝑋⃗| (22)
𝑋1
⃗⃗⃗⃗⃗ = 𝑋 𝛼
⃗⃗⃗⃗⃗ − 𝐴1
⃗⃗⃗⃗⃗ . (𝐷 𝛼
⃗⃗⃗⃗⃗⃗) (23)
𝑋2
⃗⃗⃗⃗⃗ = 𝑋𝛽
⃗⃗⃗⃗⃗ − 𝐴2
⃗⃗⃗⃗⃗ . (𝐷𝛽
⃗⃗⃗⃗⃗) (24)
𝑋3
⃗⃗⃗⃗⃗ = 𝑋 𝛿
⃗⃗⃗⃗⃗ − 𝐴3
⃗⃗⃗⃗⃗ . (𝐷𝛿
⃗⃗⃗⃗⃗) (25)
𝑋4
⃗⃗⃗⃗⃗ = 𝑋 𝜔
⃗⃗⃗⃗⃗⃗ − 𝐴3
⃗⃗⃗⃗⃗ . (𝐷 𝜔
⃗⃗⃗⃗⃗⃗) (26)
𝑋̅(𝑡 + 1) =
𝑋1⃗⃗⃗⃗⃗⃗+𝑋2⃗⃗⃗⃗⃗⃗+𝑋3⃗⃗⃗⃗⃗⃗+𝑋4⃗⃗⃗⃗⃗⃗
4
(27)
Commence,
Search agents population initiated,
a, A,C values are initiated
𝑋 𝛼
⃗⃗⃗⃗⃗ - Indicates the most excellent search agent
𝑋𝛽
⃗⃗⃗⃗⃗ - Indicates the next greatest search agent
𝑋 𝛿
⃗⃗⃗⃗⃗ - Indicates the subsequent finest search agent
While (t< maximum iteration number)
Modernize the position of the present search agent by 𝑋̅(𝑡 + 1) =
𝑋1⃗⃗⃗⃗⃗⃗+𝑋2⃗⃗⃗⃗⃗⃗+𝑋3⃗⃗⃗⃗⃗⃗+𝑋4⃗⃗⃗⃗⃗⃗
4
End for
Renew the values of a, A, C
Search agents fitness value should be computed
Modernize the values of 𝑋 𝜔
⃗⃗⃗⃗⃗⃗, 𝑋𝛽
⃗⃗⃗⃗⃗ , 𝑋 𝛿
⃗⃗⃗⃗⃗ , 𝑋 𝜔
⃗⃗⃗⃗⃗⃗
t=t+1
end while
Return with 𝑋 𝛼
⃗⃗⃗⃗⃗
4. Int J Inf & Commun Technol ISSN: 2252-8776
Real power loss reduction by tundra wolf algorithm (Kanagasabai Lenin)
103
4. SIMULATION RESULTS
At first in standard IEEE 14 bus system [18] the validity of the proposed Tundra wolf algorithm
(TWA) has been tested, Table 1 shows the constraints of control variables Table 2 shows the limits of
reactive power generators and comparison results are presented in Table 3.
Then the proposed Tundra wolf algorithm (TWA) has been tested, in IEEE 30 Bus system. Table 4
shows the constraints of control variables, Table 5 shows the limits of reactive power generators and
comparison results are presented in Table 6.
Table 1. Constraints of control variables
System Variables
Minimum
(PU)
Maximum
(PU)
IEEE 14
Bus
Generator
Voltage
0.95 1.1
Transformer
Tap
o.9 1.1
VAR Source 0 0.20
Table 2. Constrains of reactive power generators
System Variables
Q Minimum
(PU)
Q Maximum
(PU)
IEEE 14
Bus
1 0 10
2 -40 50
3 0 40
6 -6 24
8 -6 24
Table 3. Simulation results of IEEE -14 system
Control variables Base case MPSO [19] PSO [19] EP [19] SARGA [19] TWA
𝑉𝐺−1 1.060 1.100 1.100 NR* NR* 1.012
𝑉𝐺−2 1.045 1.085 1.086 1.029 1.060 1.018
𝑉𝐺−3 1.010 1.055 1.056 1.016 1.036 1.013
𝑉𝐺−6 1.070 1.069 1.067 1.097 1.099 1.018
𝑉𝐺−8 1.090 1.074 1.060 1.053 1.078 1.023
𝑇𝑎𝑝 8 0.978 1.018 1.019 1.04 0.95 0.921
𝑇𝑎𝑝 9 0.969 0.975 0.988 0.94 0.95 0.920
𝑇𝑎𝑝 10 0.932 1.024 1.008 1.03 0.96 0.929
𝑄𝐶−9 0.19 14.64 0.185 0.18 0.06 0.128
𝑃𝐺 272.39 271.32 271.32 NR* NR* 271.76
𝑄𝐺 (Mvar) 82.44 75.79 76.79 NR* NR* 75.79
Reduction in PLoss (%) 0 9.2 9.1 1.5 2.5 26.13
Total PLoss (Mw) 13.550 12.293 12.315 13.346 13.216 10.009
NR* - Not reported
Table 4. Constraints of control variables
System Variables
Minimum
(PU)
Maximum
(PU)
IEEE 30
Bus
Generator
Voltage
0.95 1.1
Transformer
Tap
o.9 1.1
VAR Source 0 0.20
Table 5. Constrains of reactive power generators
System Variables
Q Minimum
(PU)
Q Maximum
(PU)
IEEE 30
Bus
1 0 10
2 -40 50
5 -40 40
8 -10 40
11 -6 24
13 -6 24
Table 6. Simulation results of IEEE -30 system
Control variables Base case MPSO [19] PSO [19] EP [19] SARGA [19] TWA
𝑉𝐺−1 1.060 1.101 1.100 NR* NR* 1.019
𝑉𝐺−2 1.045 1.086 1.072 1.097 1.094 1.020
𝑉𝐺−5 1.010 1.047 1.038 1.049 1.053 1.018
𝑉𝐺−8 1.010 1.057 1.048 1.033 1.059 1.026
𝑉𝐺−12 1.082 1.048 1.058 1.092 1.099 1.031
VG-13 1.071 1.068 1.080 1.091 1.099 1.029
Tap11 0.978 0.983 0.987 1.01 0.99 0.935
Tap12 0.969 1.023 1.015 1.03 1.03 0.931
Tap15 0.932 1.020 1.020 1.07 0.98 0.927
Tap36 0.968 0.988 1.012 0.99 0.96 0.937
QC10 0.19 0.077 0.077 0.19 0.19 0.091
QC24 0.043 0.119 0.128 0.04 0.04 0.121
𝑃𝐺 (MW) 300.9 299.54 299.54 NR* NR* 297.69
𝑄𝐺 (Mvar) 133.9 130.83 130.94 NR* NR* 131.39
Reduction in PLoss (%) 0 8.4 7.4 6.6 8.3 20.14
Total PLoss (Mw) 17.55 16.07 16.25 16.38 16.09 14.014
5. ISSN: 2252-8776
Int J Inf & Commun Technol, Vol. 9, No. 2, August 2020: 100 – 104
104
5. CONCLUSION
In this paper Tundra wolf algorithm (TWA) successfully solved the optimal reactive power problem.
Proposed algorithm perk up the exploration capability of the Tundra wolf wolves in an extensive mode. Also
it makes the search agents to spread widely during exploration phase. In the proposed Tundra wolf algorithm
(TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first
three most excellent candidates. This mode of hunting action increases the efficiency. Proposed Tundra wolf
algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the
projected algorithm reduced the real power loss. Percentage of real power loss reduction has been improved
when compared to other standard algorithms.
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